For the COCO dataset, we use the person detection results from the previous methods [1]}, [2]} for a fair comparison. For the CrowdPose dataset, we use a cascade mask-rcnn [3]} with Swin-B backbone trained on the COCO detection dataset to generate the person detection results. We use the UDP [4]} to reduce the quantization errors brought by the heatmaps and use flip testing by averaging the heatmaps predicted by the original and flipped images during the inference.
